What database does this skill use for storage?

This skill utilizes SQLite for local, serverless storage of backtest results, providing a lightweight and portable way to manage your trading data.

How does the skill handle date and time information?

It stores dates as Unix timestamps (INTEGERS) within the SQLite database to ensure high query performance and avoid common timezone or string formatting issues.

Does it store the full equity curve for every run?

No, to prevent database bloat, the skill is designed to store high-level summary metrics and final equity values rather than full high-frequency time-series data.

Can I compare different model versions using this skill?

Yes, the schema includes fields for model paths and serialized configuration JSON, allowing you to easily compare performance across different iterations and parameters.

Backtest Result Persistence

Name: Backtest Result Persistence
Author: smith6jt-cop

bysmith6jt-cop

0•

Data Science & ML

Persists trading backtest results to an SQLite database to enable historical performance comparison and model optimization.

The Backtest Persistence skill provides a standardized framework for recording trading strategy performance metrics directly into a local SQLite database. It solves the problem of transient, in-memory results by establishing a schema that tracks symbols, timeframes, model configurations, and key financial metrics like Sharpe ratio, profit factor, and maximum drawdown. This allows developers to query historical data to find top-performing models, track version history, and maintain a permanent audit trail of strategy simulations for better data-driven decision making.

Key Features

01JSON-based configuration storage for full experiment reproducibility

02Automated persistence of financial KPIs including Sharpe ratio and Drawdown

030 GitHub stars

04Efficient querying of best-performing models via optimized SQL indexes

05Flexible CLI integration for saving results during automated runs

06Standardized SQLite schema for comprehensive backtest metrics

Use Cases

01Maintaining a historical audit trail of all strategy simulations and configurations

02Identifying the best performing asset and timeframe combinations using SQL queries

03Comparing performance across multiple iterations of a machine learning trading model

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add smith6jt-cop/skills_registry backtest-persistence

For use in Claude.ai and ChatGPT

Download Skill